Software Engineering & Artificia

Recent paper related to SE and AI

ASE(#ase)

• 2021
  • Deep GUI: Black-box GUI Input Generation with Deep Learning
  • DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning
  • DeepMemory: Model-based Memorization Analysis of Deep Neural Language Models
  • DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score
  • FIGCPS: Effective Failure-inducing Input Generation for Cyber-Physical Systems with Deep Reinforcement Learning
  • Automated Testing for Machine Translation via Constituency Invariance
  • Efficient state synchronisation in model-based testing through reinforcement learning
  • FRUGAL: Unlocking Semi-supervised Learning for Software Analytics
  • On Multi-Modal Learning of Editing Source Code

• 2020

  • Invited Talk: Smart Development of Mobile Apps with Deep Learning
  • Hybrid Deep Neural Networks to Infer State Models of Black-Box Systems
  • MARBLE: Model-Based Robustness Analysis of Stateful Deep Learning Systems
  • A Deep Multitask Learning Approach for Requirements Discovery and Annotation from Open Forum
  • Audee: Automated Testing for Deep Learning Frameworks
  • Safety and Robustness for Deep Learning with Provable Guarantees
  • When Deep Learning Meets Smart Contracts
  • Problems and Opportunities in Training Deep Learning Software Systems: An Analysis of Variance
  • BugPecker: Locating Faulty Methods with Deep Learning on Revision Graphs
  • Cats Are Not Fish: Deep Learning Testing Calls for Out-Of-Distribution Awareness
  • Towards Robust Production Machine Learning Systems: Managing Dataset Shift
  • A Machine Learning based Approach to Autogenerate Diagnostic Models for CNC machines
  • Emotion Detection in Roman Urdu Text using Machine Learning
  • Machine Learning meets Software Performance: Optimization, Transfer Learning, and Counterfactual Causal Inference

• 2019

  • A Study of Oracle Approximations in Testing Deep Learning Libraries
  • An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms
  • Apricot: A Weight-Adaptation Approach to Fixing Deep Learning Models
  • Property Inference for Deep Neural Networks
  • Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning
  • Machine Learning Based Automated Method Name Recommendation: How Far Are We

ISSTA

• 2021

  • AdvDoor: Adversarial Backdoor Attack of Deep Learning System
  • Deep Just-in-Time Defect Prediction: How Far Are We?
  • DeepCrime: Mutation Testing of Deep Learning Systems Based on Real Faults
  • DeepHyperion: Exploring the Feature Space of Deep Learning-Based Systems through Illumination Search
  • Exposing Previously Undetectable Faults in Deep Neural Networks
  • Predoo: Precision Testing of Deep Learning Operators
  • TERA: Optimizing Stochastic Regression Tests in Machine Learning Projects

• 2020

  • DeepGini: Prioritizing Massive Tests to Enhance the Robustness of Deep Neural Networks
  • DeepSQLi: Deep Semantic Learning for Testing SQL Injection
  • Effective White-Box Testing of Deep Neural Networks with Adaptive Neuron-Selection Strategy
  • Detecting Flaky Tests in Probabilistic and Machine Learning Applications
  • Detecting and Understanding Real-World Differential Performance Bugs in Machine Learning Libraries
  • Higher Income, Larger Loan? Monotonicity Testing of Machine Learning Models

• 2019

  • DeepHunter: A Coverage-Guided Fuzz Testing Framework for Deep Neural Networks
  • Search-based Test and Improvement of Machine-Learning-Based Anomaly Detection Systems

ICSE

• 2021
  • An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications
  • DeepBackdoor: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection
  • DeepLV: Suggesting Log Levels Using Ordinal Based Neural Networks
  • DeepLocalize: Fault Localization for Deep Neural Networks
  • Graph-based Fuzz Testing for Deep Learning Inference Engines
  • Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models
  • Prioritizing Test Inputs for Deep Neural Networks via Mutation Analysis
  • RobOT: Robustness-Oriented Testing for Deep Learning Systems
  • Scalable Quantitative Verification For Deep Neural Networks
  • Self-Checking Deep Neural Networks in Deployment
  • An Empirical Study of Refactorings and Technical Debt in Machine Learning Systems
  • Are Machine Learning Cloud APIs Used Correctly?
  • Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We?
  • CURE: Code-Aware Neural Machine Translation for Automatic Program Repair
  • Testing Machine Translation via Referential Transparency
  • White-Box Analysis over Machine Learning: Modeling Performance of Configurable Systems

• 2020
  • An Empirical Study on Program Failures of Deep Learning Jobs
  • DISSECTOR: Input Validation for Deep Learning Applications by Crossing-layer Dissection
  • Detection of Hidden Feature Requests from Massive Chat Messages via Deep Siamese Network
  • Fuzz Testing based Data Augmentation to Improve Robustness of Deep Neural Networks
  • Importance-Driven Deep Learning System Testing
  • ReluDiff: Differential Verification of Deep Neural Networks
  • Repairing Deep Neural Networks: Fix Patterns and Challenges
  • Software Visualization and Deep Transfer Learning for Effective Software Defect Prediction
  • Taxonomy of Real Faults in Deep Learning Systems
  • Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty
  • Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning
  • Automatic Testing and Improvement of Machine Translation
  • Structure-Invariant Testing for Machine Translation

• 2019
  • Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
  • CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries
  • DeepPerf: Performance Prediction for Configurable Software with Deep Sparse Neural Network
  • Guiding Deep Learning System Testing using Surprise Adequacy
  • On Learning Meaningful Code Changes via Neural Machine Translation

ESEC/FSE

• 2021

  • A Comprehensive Study of Deep Learning Compiler Bugs
  • Exposing Numerical Bugs in Deep Learning via Gradient Back-Propagation
  • Bias in Machine Learning Software: Why? How? What to Do?
  • Explaining Mispredictions of Machine Learning Models using Rule Induction
  • FLEX: Fixing Flaky Tests in Machine Learning Projects by Updating Assertion Bounds
  • Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline

• 2020

  • A Comprehensive Study on Challenges in Deploying Deep Learning Based Software
  • Correlations between Deep Neural Network Model Coverage Criteria and Model Quality
  • Deep Learning Library Testing via Effective Model Generation
  • DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks
  • Dynamic Slicing for Deep Neural Networks
  • Is Neuron Coverage a Meaningful Measure for Testing Deep Neural Networks?
  • Model-Based Exploration of the Frontier of Behaviours for Deep Learning System Testing
  • Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?
  • On Decomposing a Deep Neural Network into Modules
  • Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness
  • Machine Translation Testing via Pathological Invariance
  • Mining Assumptions for Software Components using Machine Learning

• 2019

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IJCAI

• 2021

  • BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing

• 2020

  • Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models

AAAI

• 2021

  • Group Testing on a Network
  • Testing Independence between Linear Combinations for Causal Discovery

• 2020

  • A MaxSAT-based Framework for Group Testing
  • A New Framework for Online Testing of Heterogeneous Treatment Effect

• 2019

  • On Testing of Samplers
  • DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing

NeurIPS

• 2020

  • On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law
  • A/B Testing in Dense Large-Scale Networks: Design and Inference

• 2019

  • Online Neural Connectivity Estimation with Noisy Group Testing
  • Private Identity Testing for High-Dimensional Distributions
  • Testing Determinantal Point Processes

ICML

• 2021

  • Exploiting structured data for learning contagious diseases under incomplete testing
  • Robust Testing and Estimation under Manipulation Attacks
  • Active Testing: Sample-Efficient Model Evaluation
  • Testing DNN-based Autonomous Driving Systems under Critical Environmental Conditions

• 2020

  • Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing
  • Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making

• 2019

  • Conditional Independence in Testing Bayesian Networks
  • Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits